Systematic generation and analysis of counterfactuals for compound activity predictions using multi-task models-Reference-Cited by-同舟云学术

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Systematic generation and analysis of counterfactuals for compound activity predictions using multi-task models

Published:2024 Issue:5 Volume:15 Page:1547-1555
ISSN:2632-8682
Container-title:RSC Medicinal Chemistry
language:en
Short-container-title:RSC Med. Chem.

Author:

Lamens Alec¹,Bajorath Jürgen¹²^ORCID

Affiliation:

1. Department of Life Science Informatics and Data Science, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany

2. Lamarr Institute for Machine Learning and Artificial Intelligence, Rheinische Friedrich-Wilhelms-Universität Bonn, Friedrich-Hirzebruch-Allee 5/6, D-53115 Bonn, Germany

Abstract

For a kinase inhibitor correctly predicted with a multi-task machine learning model (shown on an orange background), counterfactuals with small chemical changes (shown in red) were generated that were predicted to be active against other kinases.

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/MD/D4MD00128A

Reference34 articles.

1. Computational approaches streamlining drug discovery

2. Machine Learning in Chemoinformatics and Medicinal Chemistry

3. Applications of machine learning in drug discovery and development

4. The rise of deep learning in drug discovery

5. Why black box machine learning should be avoided for high-stakes decisions, in brief

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